Google Professional Machine Learning Engineer
Get started today
Ultimate access to all questions.
Your company is building a unified analytics environment spanning a variety of on-premises data marts. The organization is currently facing significant data quality and security challenges during data integration due to using a wide range of disconnected tools and temporary solutions. The goal is to identify a fully managed, cloud-native data integration service that will reduce the overall cost of operations and minimize repetitive tasks. Furthermore, some team members prefer using a codeless interface for developing Extract, Transform, Load (ETL) processes. Given these requirements, which service should you choose?
Your company is building a unified analytics environment spanning a variety of on-premises data marts. The organization is currently facing significant data quality and security challenges during data integration due to using a wide range of disconnected tools and temporary solutions. The goal is to identify a fully managed, cloud-native data integration service that will reduce the overall cost of operations and minimize repetitive tasks. Furthermore, some team members prefer using a codeless interface for developing Extract, Transform, Load (ETL) processes. Given these requirements, which service should you choose?
Explanation:
The correct answer is D. Cloud Data Fusion is specifically designed as a fully managed, cloud-native data integration service that simplifies building and managing ETL pipelines across varied data sources. It offers a codeless interface, which meets the preference of some team members for a code-free environment for ETL process development. This makes it a suitable choice for addressing the company's data quality and security challenges while reducing repetitive work and lowering operational costs.